Repository: biocypher/biochatter (language: python)

Issue:
Added create runnable test for Anndata



Discussion / hints:

Fix patch:
diff --git a/benchmark/data/benchmark_api_calling_data.yaml b/benchmark/data/benchmark_api_calling_data.yaml
index 3af50a0d..41ce4e21 100644
--- a/benchmark/data/benchmark_api_calling_data.yaml
+++ b/benchmark/data/benchmark_api_calling_data.yaml
@@ -15,64 +15,76 @@
 # '.', etc., by adding two backslashes before them.
 
 api_calling:
-  - case: oncokb:braf:melanoma
-    input:
-      prompt:
-        exact_spelling: "What is the consequence of the V600E BRAF variant in Melanoma?"
-    expected:
-      parts_of_query:
-        [
-          "https://demo.oncokb.org/api/v1/annotate/mutations/byProteinChange\\?",
-          "hugoSymbol=BRAF",
-          "alteration=V600E",
-          "tumorType=Melanoma",
-        ]
-  - case: oncokb:tp53:colon_adenocarcinoma
-    input:
-      prompt:
-        exact_spelling: "What is the consequence of the R273C TP53 variant in Colon Adenocarcinoma?"
-    expected:
-      parts_of_query:
-        [
-          "https://demo.oncokb.org/api/v1/annotate/mutations/byProteinChange\\?",
-          "hugoSymbol=TP53",
-          "alteration=R273C",
-          "tumorType=Colon%20Adenocarcinoma",
-        ]
-  - case: oncokb:braf:histiocytosis
-    input:
-      prompt:
-        exact_spelling: "What is the consequence of the N486_P490del BRAF variant in Histiocytosis?"
-        descriptive_spelling: "What is the consequence of an N486_P490 deletion in BRAF in Histiocytosis?"
-    expected:
-      parts_of_query:
-        [
-          "https://demo.oncokb.org/api/v1/annotate/mutations/byProteinChange\\?",
-          "hugoSymbol=BRAF",
-          "alteration=N486_P490del",
-          "tumorType=Histiocytosis",
-        ]
-  - case: oncokb:ros1:lung_adenocarcinoma
-    input:
-      prompt:
-        exact_spelling: "What is the consequence of the functional fusion of CD47 and ROS1 in Lung Adenocarcinoma?"
-    expected:
-      parts_of_query:
-        [
-          "https://demo.oncokb.org/api/v1/annotate/structuralVariants\\?",
-          "hugoSymbolA=CD74",
-          "hugoSymbolB=ROS1",
-          "structuralVariantType=FUSION",
-          "isFunctionalFusion=true",
-          "tumorType=Lung%20Adenocarcinoma",
-        ]
-  - case: biotools:topic:metabolomics
-    input:
-      prompt:
-        fuzzy_search: "Which tools can I use for metabolomics?"
-    expected:
-      parts_of_query:
-        ["https://bio.tools/api/t/", "\\?topic=", "[mM]etabolomics"]
+  # - case: oncokb:braf:melanoma
+  #   input:
+  #     prompt:
+  #       exact_spelling: "What is the consequence of the V600E BRAF variant in Melanoma?"
+  #   expected:
+  #     parts_of_query:
+  #       [
+  #         "https://demo.oncokb.org/api/v1/annotate/mutations/byProteinChange\\?",
+  #         "hugoSymbol=BRAF",
+  #         "alteration=V600E",
+  #         "tumorType=Melanoma",
+  #       ]
+  # - case: oncokb:tp53:colon_adenocarcinoma
+  #   input:
+  #     prompt:
+  #       exact_spelling: "What is the consequence of the R273C TP53 variant in Colon Adenocarcinoma?"
+  #   expected:
+  #     parts_of_query:
+  #       [
+  #         "https://demo.oncokb.org/api/v1/annotate/mutations/byProteinChange\\?",
+  #         "hugoSymbol=TP53",
+  #         "alteration=R273C",
+  #         "tumorType=Colon%20Adenocarcinoma",
+  #       ]
+  # - case: oncokb:braf:histiocytosis
+  #   input:
+  #     prompt:
+  #       exact_spelling: "What is the consequence of the N486_P490del BRAF variant in Histiocytosis?"
+  #       descriptive_spelling: "What is the consequence of an N486_P490 deletion in BRAF in Histiocytosis?"
+  #   expected:
+  #     parts_of_query:
+  #       [
+  #         "https://demo.oncokb.org/api/v1/annotate/mutations/byProteinChange\\?",
+  #         "hugoSymbol=BRAF",
+  #         "alteration=N486_P490del",
+  #         "tumorType=Histiocytosis",
+  #       ]
+  # - case: oncokb:ros1:lung_adenocarcinoma
+  #   input:
+  #     prompt:
+  #       exact_spelling: "What is the consequence of the functional fusion of CD47 and ROS1 in Lung Adenocarcinoma?"
+  #   expected:
+  #     parts_of_query:
+  #       [
+  #         "https://demo.oncokb.org/api/v1/annotate/structuralVariants\\?",
+  #         "hugoSymbolA=CD74",
+  #         "hugoSymbolB=ROS1",
+  #         "structuralVariantType=FUSION",
+  #         "isFunctionalFusion=true",
+  #         "tumorType=Lung%20Adenocarcinoma",
+  #       ]
+  # - case: biotools:topic:metabolomics
+  #   input:
+  #     prompt:
+  #       fuzzy_search: "Which tools can I use for metabolomics?"
+  #   expected:
+  #     parts_of_query:
+  #       ["https://bio.tools/api/t/", "\\?topic=", "[mM]etabolomics"]
+  # - case: scanpy:tl:leiden
+  #   input:
+  #     prompt:
+  #       explicit_variable_names: "Perform Leiden clustering on the data with resolution 0.5."
+  #   expected:
+  #     parts_of_query: ["sc.tl.leiden\\(", "resolution=0.5", "\\)"]
+  # - case: scanpy:tl:umap
+  #   input:
+  #     prompt:
+  #       explicit_variable_names: "Calculate UMAP embedding with minimum distance 0.3 and spread 1.0."
+  #   expected:
+  #     parts_of_query: ["sc.tl.umap\\(", "min_dist=0.3", "spread=1.0", "\\)"]
   - case: scanpy:tl:leiden
     input:
       prompt:
@@ -160,6 +172,69 @@ api_calling:
   - case: anndata:read:h5ad
     input:
       prompt:
-        explicit_variable_names: "read test.h5ad into an anndata object."
+        explicit_variable_names: "Use AnnData to load the file test.h5ad into an AnnData object."
+        specific: "Load test.h5ad using AnnData."
+        abbreviation: "Read test.h5ad with AnnData."
+        general: "Open an H5AD file and load it as an AnnData object."
+        help_request: "How do I read test.h5ad into an AnnData object?"
     expected:
-      parts_of_query: ["anndata.read_h5ad\\(", "filename=test.h5ad", "\\)"]
+      parts_of_query: ["anndata.io.read_h5ad\\(", "filename=test.h5ad", "\\)"]
+  - case: anndata:read:csv
+    input:
+      prompt:
+        explicit_variable_names: "Use AnnData to load the file `test.csv` into an AnnData object."
+        specific: "Load test.csv using AnnData."
+        abbreviation: "Read test.csv with AnnData."
+        general: "Open a CSV file and load it as an AnnData object."
+        help_request: "How do I read test.csv into an AnnData object?"
+    expected:
+      parts_of_query: ["anndata.io.read_csv\\(", "filename=test.csv", "\\)"]
+  - case: anndata:concat:var
+    input:
+      prompt:
+        explicit_variable_names: "Concatenate adata1 and adata2 into a single AnnData object along the column axis with an inner join."
+        specific: "Join adata1 and adata2 by columns using AnnData with an inner join."
+        abbreviation: "Merge columns of adata1 and adata2 with AnnData."
+        general: "Combine two AnnData objects along the variable axis with an inner join."
+        help_request: "How do I concatenate adata1 and adata2 along columns?"
+    expected:
+      parts_of_query:
+        [
+          "anndata.concat\\(",
+          "\\[adata1, adata2\\]",
+          ", axis='var', join='inner'",
+          "\\)",
+        ]
+  - case: anndata:concat:obs
+    input:
+      prompt:
+        explicit_variable_names: "Concatenate adata1 and adata2 into a single AnnData object along the row axis with an outer join."
+        specific: "Join adata1 and adata2 by rows using AnnData with an outer join."
+        abbreviation: "Merge rows of adata1 and adata2 with AnnData."
+        general: "Combine two AnnData objects along the observation axis with an outer join."
+        help_request: "How do I concatenate adata1 and adata2 along rows?"
+    expected:
+      parts_of_query:
+        [
+          "anndata.concat\\(",
+          "\\[adata1, adata2\\]",
+          ", axis='obs', join='outer'",
+          "\\)",
+        ]
+  - case: anndata:map
+    input:
+      prompt:
+        explicit_variable_names: "Replace the values in the cell_type column of the obs attribute in adata. Replace type1 with new_type1, type2 with new_type2, and type3 with new_type3."
+        help_request: "How do I remap cell_type values to replace type1 with new_type1, type2 with new_type2, and type3 with new_type3. ?"
+    expected:
+      parts_of_query:
+        [
+          "adata.obs",
+          "\\[\"cell_type\"\\]",
+          "\\.map\\(",
+          "\\{\\s*\"type1\": \"new_type1\"",
+          "\\s*\"type2\": \"new_type2\"",
+          "\\s*\"type3\": \"new_type3\"",
+          "\\s*\\}\\)",
+          "\\.copy\\(\\)",
+        ]
\ No newline at end of file
diff --git a/benchmark/results/processed/api_calling_benchmark_summary.csv b/benchmark/results/processed/api_calling_benchmark_summary.csv
new file mode 100644
index 00000000..6bfb43ef
--- /dev/null
+++ b/benchmark/results/processed/api_calling_benchmark_summary.csv
@@ -0,0 +1,3 @@
+benchmark_type,mean,std,min,max,count
+Python API,0.474,0.293,0.0,0.8,367
+Python API (Reduced),0.753,0.046,0.6,0.8,191
diff --git a/benchmark/results/processed/api_calling_summary.csv b/benchmark/results/processed/api_calling_summary.csv
new file mode 100644
index 00000000..a47e693e
--- /dev/null
+++ b/benchmark/results/processed/api_calling_summary.csv
@@ -0,0 +1,17 @@
+benchmark_type,model_name,mean,std,min,max,count
+Python API,gpt-3.5-turbo-0125,0.609,0.197,0.25,0.8,46
+Python API,gpt-4-0125-preview,0.508,0.282,0.0,0.8,46
+Python API,gpt-4-0613,0.487,0.282,0.0,0.8,46
+Python API,gpt-4-1106-preview,0.61,0.219,0.0,0.8,45
+Python API,gpt-4-turbo-2024-04-09,0.27,0.293,0.0,0.8,46
+Python API,gpt-4o-2024-08-06,0.363,0.308,0.0,0.8,46
+Python API,gpt-4o-2024-11-20,0.381,0.318,0.0,0.8,46
+Python API,gpt-4o-mini-2024-07-18,0.572,0.239,0.0,0.8,46
+Python API (Reduced),gpt-3.5-turbo-0125,0.758,0.041,0.6,0.8,24
+Python API (Reduced),gpt-4-0125-preview,0.767,0.024,0.75,0.8,24
+Python API (Reduced),gpt-4-0613,0.767,0.024,0.75,0.8,24
+Python API (Reduced),gpt-4-1106-preview,0.765,0.024,0.75,0.8,23
+Python API (Reduced),gpt-4-turbo-2024-04-09,0.761,0.031,0.667,0.8,24
+Python API (Reduced),gpt-4o-2024-08-06,0.742,0.058,0.6,0.8,24
+Python API (Reduced),gpt-4o-2024-11-20,0.733,0.064,0.6,0.8,24
+Python API (Reduced),gpt-4o-mini-2024-07-18,0.733,0.064,0.6,0.8,24
diff --git a/benchmark/results/processed/api_calling_task_summary.csv b/benchmark/results/processed/api_calling_task_summary.csv
new file mode 100644
index 00000000..f0b040a3
--- /dev/null
+++ b/benchmark/results/processed/api_calling_task_summary.csv
@@ -0,0 +1,9 @@
+benchmark_type,task_type,mean,std,min,max,count
+Python API,abbreviation,0.5,0.295,0.0,0.8,80
+Python API,general,0.476,0.316,0.0,0.8,79
+Python API,help_request,0.388,0.296,0.0,0.8,88
+Python API,specific,0.52,0.261,0.0,0.8,120
+Python API (Reduced),abbreviation,0.753,0.046,0.6,0.8,47
+Python API (Reduced),general,0.758,0.04,0.6,0.8,48
+Python API (Reduced),help_request,0.754,0.046,0.6,0.8,48
+Python API (Reduced),specific,0.747,0.051,0.6,0.8,48
diff --git a/benchmark/results/processed/overview-model.csv b/benchmark/results/processed/overview-model.csv
index 4a0f91ea..16d697a4 100644
--- a/benchmark/results/processed/overview-model.csv
+++ b/benchmark/results/processed/overview-model.csv
@@ -1,18 +1,20 @@
 Model name,Size,Median Accuracy,SD
-gpt-3.5-turbo-0125,175,0.79,0.2
-gpt-4o-2024-08-06,Unknown,0.78,0.24
 claude-3-opus-20240229,Unknown,0.77,0.28
+gpt-3.5-turbo-0125,175,0.77,0.19
 gpt-3.5-turbo-0613,175,0.76,0.21
 claude-3-5-sonnet-20240620,Unknown,0.76,0.28
 llama-3.1-instruct,70,0.73,0.29
 gpt-4-0613,Unknown,0.73,0.17
+gpt-4o-2024-08-06,Unknown,0.73,0.24
 llama-3.1-instruct,8,0.72,0.28
-gpt-4-turbo-2024-04-09,Unknown,0.71,0.26
-gpt-4o-mini-2024-07-18,Unknown,0.69,0.23
-gpt-4-0125-preview,Unknown,0.69,0.27
+gpt-4o-mini-2024-07-18,Unknown,0.69,0.22
+gpt-4-0125-preview,Unknown,0.69,0.26
+gpt-4-1106-preview,Unknown,0.69,0.06
 gpt-4o-2024-05-13,Unknown,0.68,0.31
+gpt-4-turbo-2024-04-09,Unknown,0.68,0.27
 llama-3-instruct,8,0.65,0.36
 openhermes-2.5,7,0.6,0.3
+gpt-4o-2024-11-20,Unknown,0.55,0.14
 chatglm3,6,0.44,0.26
 llama-2-chat,70,0.42,0.34
 code-llama-instruct,7,0.4,0.35
diff --git a/benchmark/results/processed/overview-quantisation.csv b/benchmark/results/processed/overview-quantisation.csv
index ef525ce5..54923bc1 100644
--- a/benchmark/results/processed/overview-quantisation.csv
+++ b/benchmark/results/processed/overview-quantisation.csv
@@ -1,7 +1,6 @@
 Model name,Size,Version,Quantisation,Median Accuracy,SD
-gpt-3.5-turbo-0125,175,,,0.79,0.2
-gpt-4o-2024-08-06,Unknown,,,0.78,0.24
 claude-3-opus-20240229,Unknown,,,0.77,0.28
+gpt-3.5-turbo-0125,175,,,0.77,0.19
 claude-3-5-sonnet-20240620,Unknown,,,0.76,0.28
 gpt-3.5-turbo-0613,175,,,0.76,0.21
 llama-3.1-instruct,8,ggufv2,Q6_K,0.74,0.28
@@ -9,13 +8,15 @@ llama-3.1-instruct,70,ggufv2,IQ2_M,0.74,0.29
 llama-3.1-instruct,8,ggufv2,Q5_K_M,0.74,0.28
 gpt-4-0613,Unknown,,,0.73,0.17
 llama-3.1-instruct,70,ggufv2,IQ4_XS,0.73,0.29
+gpt-4o-2024-08-06,Unknown,,,0.73,0.24
 llama-3.1-instruct,8,ggufv2,Q8_0,0.72,0.3
-gpt-4-turbo-2024-04-09,Unknown,,,0.71,0.26
 llama-3.1-instruct,8,ggufv2,Q3_K_L,0.71,0.28
 llama-3.1-instruct,8,ggufv2,Q4_K_M,0.7,0.26
 llama-3.1-instruct,8,ggufv2,IQ4_XS,0.69,0.28
-gpt-4-0125-preview,Unknown,,,0.69,0.27
-gpt-4o-mini-2024-07-18,Unknown,,,0.69,0.23
+gpt-4-1106-preview,Unknown,,,0.69,0.06
+gpt-4-0125-preview,Unknown,,,0.69,0.26
+gpt-4o-mini-2024-07-18,Unknown,,,0.69,0.22
+gpt-4-turbo-2024-04-09,Unknown,,,0.68,0.27
 gpt-4o-2024-05-13,Unknown,,,0.68,0.31
 llama-3.1-instruct,70,ggufv2,Q3_K_S,0.67,0.28
 llama-3-instruct,8,ggufv2,Q8_0,0.65,0.35
@@ -27,6 +28,7 @@ openhermes-2.5,7,ggufv2,Q5_K_M,0.6,0.29
 openhermes-2.5,7,ggufv2,Q8_0,0.6,0.3
 openhermes-2.5,7,ggufv2,Q4_K_M,0.6,0.3
 openhermes-2.5,7,ggufv2,Q3_K_M,0.56,0.3
+gpt-4o-2024-11-20,Unknown,,,0.55,0.14
 code-llama-instruct,34,ggufv2,Q2_K,0.5,0.33
 openhermes-2.5,7,ggufv2,Q2_K,0.5,0.28
 code-llama-instruct,7,ggufv2,Q3_K_M,0.49,0.31
diff --git a/benchmark/results/processed/overview.csv b/benchmark/results/processed/overview.csv
index 9ca55d49..139b4a2d 100644
--- a/benchmark/results/processed/overview.csv
+++ b/benchmark/results/processed/overview.csv
@@ -1,77 +1,79 @@
-Full model name,property_selection,query_generation,multimodal_answer,api_calling,sourcedata_info_extraction,explicit_relevance_of_single_fragments,entity_selection,relationship_selection,end_to_end_query_generation,naive_query_generation_using_schema,implicit_relevance_of_multiple_fragments,medical_exam,property_exists,Mean Accuracy,Median Accuracy,SD
-gpt-3.5-turbo-0125,0.3674698795180723,0.953757225433526,,0.7464788732394366,0.5100318961757607,1.0,0.9782608695652174,1.0,0.9190751445086706,0.5144508670520231,0.9,0.6713221601489758,0.7894736842105263,0.7791933833210174,0.7894736842105263,0.2012911551108504
-gpt-4o-2024-08-06,0.425438596491228,0.8742138364779874,,,0.7111847677854639,1.0,1.0,1.0,0.8301886792452831,0.5283018867924528,0.6666666666666666,0.8502109704641351,0.1798941798941798,0.7332817803470362,0.7817352297961596,0.24263416649198286
-claude-3-opus-20240229,0.421875,0.9444444444444444,,,0.6912346497167026,0.8333333333333334,1.0,0.0,0.6555555555555556,0.7333333333333333,1.0,0.8055555555555556,1.0,0.7350301701762659,0.7702928628659107,0.27641140414857135
-claude-3-5-sonnet-20240620,0.375,0.9666666666666668,,,0.7560882840829611,1.0,1.0,0.0,0.7333333333333333,0.6333333333333333,1.0,0.7737003058103975,0.8666666666666667,0.7367989627175782,0.7648942949466793,0.283501896458108
-gpt-3.5-turbo-0613,0.3625,0.9466666666666668,,,0.5753814654033865,1.0,0.8888888888888888,0.5,0.8333333333333334,0.5,1.0,,0.7555555555555555,0.7362325909847831,0.7555555555555555,0.21192578872727258
-llama-3.1-instruct:8:ggufv2:Q6_K,0.46875,0.9555555555555556,,,0.3944693874460002,1.0,1.0,0.0,0.7333333333333333,0.6333333333333333,0.8333333333333334,0.7517482517482518,0.8333333333333334,0.6912596843711946,0.7425407925407925,0.2780153310368451
-llama-3.1-instruct:70:ggufv2:IQ2_M,0.328125,0.9555555555555556,,,0.6264977614977616,1.0,1.0,0.0,0.6,0.6333333333333333,1.0,0.7728813559322034,0.9166666666666666,0.7120963339077746,0.742488844919989,0.2936758323217435
-llama-3.1-instruct:8:ggufv2:Q5_K_M,0.4375,0.9333333333333332,,,0.3804767994461107,1.0,1.0,0.0,0.7333333333333333,0.7,0.8333333333333334,0.7491166077738516,0.8333333333333334,0.690947885504845,0.7412249705535925,0.27927836616249907
-gpt-4-0613,0.3825301204819277,0.9595375722543352,,0.6190476190476191,0.6689027994568157,1.0,0.9206349206349206,0.6956521739130435,0.8786127167630058,0.6820809248554913,1.0,0.7309124767225326,0.6666666666666666,0.7670481658996965,0.7309124767225326,0.17229741694027406
-llama-3.1-instruct:70:ggufv2:IQ4_XS,0.375,0.9555555555555556,,,0.6992381352139458,1.0,1.0,0.0,0.6,0.5666666666666667,1.0,0.8225806451612904,0.75,0.7062764547815871,0.7281382273907935,0.285225912616868
-llama-3.1-instruct:8:ggufv2:Q8_0,0.5657894736842105,0.9371069182389936,,,0.3890703704343646,1.0,1.0,0.1428571428571428,0.7735849056603774,0.660377358490566,1.0,0.7657342657342657,0.1617647058823529,0.6723895582711158,0.7190619120026908,0.29523232609113254
-gpt-4-turbo-2024-04-09,0.3253012048192771,0.8323699421965318,0.99,,0.6503689591957673,1.0,1.0,0.1304347826086956,0.6358381502890174,0.5086705202312138,1.0,0.8370098039215687,0.6571428571428571,0.7139280183670774,0.7139280183670774,0.2628092351854863
-llama-3.1-instruct:8:ggufv2:Q3_K_L,0.4868421052631579,0.9433962264150944,,,0.3603792487665394,1.0,1.0,0.1428571428571428,0.8113207547169812,0.6226415094339622,0.8333333333333334,0.7684210526315789,0.24,0.6553810339470718,0.7119010432893254,0.28046296548853367
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+Full model name,property_selection,query_generation,multimodal_answer,api_calling,python_api_calling,sourcedata_info_extraction,explicit_relevance_of_single_fragments,entity_selection,relationship_selection,end_to_end_query_generation,naive_query_generation_using_schema,implicit_relevance_of_multiple_fragments,python_api_calling_reduced,medical_exam,property_exists,Mean Accuracy,Median Accuracy,SD
+claude-3-opus-20240229,0.421875,0.9444444444444444,,,,0.6912346497167026,0.8333333333333334,1.0,0.0,0.6555555555555556,0.7333333333333333,1.0,,0.8055555555555556,1.0,0.735030170176266,0.7702928628659108,0.2764114041485714
+gpt-3.5-turbo-0125,0.3674698795180723,0.953757225433526,,0.7464788732394366,0.6157894736842106,0.5100318961757607,1.0,0.9782608695652174,1.0,0.9190751445086706,0.5144508670520231,0.9,0.7596153846153846,0.6713221601489758,0.7894736842105263,0.766123247010843,0.766123247010843,0.19171681897743764
+claude-3-5-sonnet-20240620,0.375,0.9666666666666668,,,,0.7560882840829611,1.0,1.0,0.0,0.7333333333333333,0.6333333333333333,1.0,,0.7737003058103975,0.8666666666666667,0.7367989627175782,0.7648942949466793,0.283501896458108
+gpt-3.5-turbo-0613,0.3625,0.9466666666666668,,,,0.5753814654033865,1.0,0.8888888888888888,0.5,0.8333333333333334,0.5,1.0,,,0.7555555555555555,0.7362325909847831,0.7555555555555555,0.21192578872727255
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+gpt-4o-2024-08-06,0.425438596491228,0.8742138364779874,,,0.3631578947368421,0.7111847677854639,1.0,1.0,1.0,0.8301886792452831,0.5283018867924528,0.6666666666666666,0.7403846153846154,0.8502109704641351,0.1798941798941798,0.7053570841491427,0.7257846915850397,0.2437187019876815
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+gpt-4-1106-preview,,,,,0.6162162162162163,,,,,,,,0.7676767676767676,,,0.6919464919464919,0.6919464919464919,0.06183351120648405
+gpt-4-0125-preview,0.0301204819277108,0.838150289017341,,0.7936507936507936,0.5087719298245614,0.6897052189771663,1.0,0.8245614035087719,0.782608695652174,0.1098265895953757,0.4566473988439306,0.5,0.7692307692307693,0.7771587743732591,0.6190476190476191,0.6213914259749623,0.6897052189771663,0.2592679484352949
+gpt-4o-mini-2024-07-18,0.3885542168674699,0.953757225433526,0.98,0.7142857142857143,0.5789473684210527,0.6845534288609352,0.8333333333333334,0.9210526315789472,0.1304347826086956,0.6878612716763006,0.5375722543352601,0.5,0.7307692307692307,0.8407960199004975,0.524390243902439,0.6670871814648935,0.6862073502686179,0.21624185648091213
+gpt-4-turbo-2024-04-09,0.3253012048192771,0.8323699421965318,0.99,,0.2596491228070175,0.6503689591957673,1.0,1.0,0.1304347826086956,0.6358381502890174,0.5086705202312138,1.0,0.7628205128205128,0.8370098039215687,0.6571428571428571,0.6849718468594614,0.6849718468594614,0.27017455790556244
+gpt-4o-2024-05-13,0.0301204819277108,0.8092485549132948,0.96,0.8095238095238095,,0.6539462799425529,1.0,1.0,0.1304347826086956,0.1156069364161849,0.5375722543352601,0.7,,0.7635009310986964,0.5263157894736842,0.6181746015569145,0.6769731399712764,0.3122588210667386
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+llama-3-instruct:8:ggufv2:Q6_K,0.28125,0.9266666666666666,,,,0.1626570670777092,1.0,0.875,0.0,0.0,0.6666666666666666,1.0,,0.6239554317548747,0.775,0.5737450756514471,0.6453110492107706,0.35916512018530455
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+gpt-4o-2024-11-20,,,,,0.3771929824561403,,,,,,,,0.7307692307692307,,,0.5539811066126855,0.5539811066126855,0.14434689892244548
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diff --git a/benchmark/results/processed/python_api_calling.csv b/benchmark/results/processed/python_api_calling.csv
new file mode 100644
index 00000000..4f1520eb
--- /dev/null
+++ b/benchmark/results/processed/python_api_calling.csv
@@ -0,0 +1,9 @@
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diff --git a/benchmark/results/processed/python_api_calling_reduced.csv b/benchmark/results/processed/python_api_calling_reduced.csv
new file mode 100644
index 00000000..20842507
--- /dev/null
+++ b/benchmark/results/processed/python_api_calling_reduced.csv
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diff --git a/benchmark/results/python_api_calling.csv b/benchmark/results/python_api_calling.csv
new file mode 100644
index 00000000..25c9cf40
--- /dev/null
+++ b/benchmark/results/python_api_calling.csv
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diff --git a/benchmark/results/python_api_calling_confidence.csv b/benchmark/results/python_api_calling_confidence.csv
new file mode 100644
index 00000000..fcf83b62
--- /dev/null
+++ b/benchmark/results/python_api_calling_confidence.csv
@@ -0,0 +1,1 @@
+model_name,subtask,correct_confidence,incorrect_confidence,md5_hash,datetime
diff --git a/benchmark/results/python_api_calling_failure_modes.csv b/benchmark/results/python_api_calling_failure_modes.csv
new file mode 100644
index 00000000..897f37bd
--- /dev/null
+++ b/benchmark/results/python_api_calling_failure_modes.csv
@@ -0,0 +1,1 @@
+model_name,subtask,actual_answer,expected_answer,failure_modes,md5_hash,datetime
diff --git a/benchmark/results/python_api_calling_reduced.csv b/benchmark/results/python_api_calling_reduced.csv
new file mode 100644
index 00000000..01636826
--- /dev/null
+++ b/benchmark/results/python_api_calling_reduced.csv
@@ -0,0 +1,192 @@
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+gpt-4o-mini-2024-07-18,scanpy:pl:scatter:specific,4;4;4/5,3,f3b608155f89ec09d93572a1ae582c95,2024-12-13 08:57:14,0.7.10
+gpt-4o-mini-2024-07-18,scanpy:pl:spatial:abbreviations,3;3;3/4,3,1cc2878a96d60e332d7351ba6407040d,2024-12-13 08:58:08,0.7.10
+gpt-4o-mini-2024-07-18,scanpy:pl:spatial:general_question,3;3;3/4,3,436ff286d626d46c0247d4d58cf8c3ff,2024-12-13 08:58:12,0.7.10
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+gpt-4o-mini-2024-07-18,scanpy:pl:tsne:abbreviations,3;3;3/4,3,ccf5bc97c2b1f0d8c96614bea9336baa,2024-12-13 08:59:02,0.7.10
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+gpt-4o-mini-2024-07-18,scanpy:pl:tsne:help_request,3;3;3/4,3,f8d9ad21a8959a748d3cc6dd68f2183b,2024-12-13 08:57:26,0.7.10
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+gpt-4o-mini-2024-07-18,scanpy:pl:umap:specific,3;3;3/4,3,c6438ed5dd43381b6e5cafe34759c09d,2024-12-13 08:57:29,0.7.10
diff --git a/benchmark/results/python_api_calling_reduced_confidence.csv b/benchmark/results/python_api_calling_reduced_confidence.csv
new file mode 100644
index 00000000..fcf83b62
--- /dev/null
+++ b/benchmark/results/python_api_calling_reduced_confidence.csv
@@ -0,0 +1,1 @@
+model_name,subtask,correct_confidence,incorrect_confidence,md5_hash,datetime
diff --git a/benchmark/results/python_api_calling_reduced_failure_modes.csv b/benchmark/results/python_api_calling_reduced_failure_modes.csv
new file mode 100644
index 00000000..897f37bd
--- /dev/null
+++ b/benchmark/results/python_api_calling_reduced_failure_modes.csv
@@ -0,0 +1,1 @@
+model_name,subtask,actual_answer,expected_answer,failure_modes,md5_hash,datetime
diff --git a/biochatter/api_agent/abc.py b/biochatter/api_agent/abc.py
index d19e87ce..3bd60ad7 100644
--- a/biochatter/api_agent/abc.py
+++ b/biochatter/api_agent/abc.py
@@ -164,7 +164,8 @@ class BaseAPIModel(BaseModel):
     """
 
     uuid: str | None = Field(
-        None, description="Unique identifier for the model instance"
+        None,
+        description="Unique identifier for the model instance",
     )
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
diff --git a/biochatter/api_agent/anndata_agent.py b/biochatter/api_agent/anndata_agent.py
index 46089bf1..2f2202ca 100644
--- a/biochatter/api_agent/anndata_agent.py
+++ b/biochatter/api_agent/anndata_agent.py
@@ -8,22 +8,20 @@
 # 4. Write the anndata object to [xxx] format -> built-in anndata api
 
 from collections.abc import Callable
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 from langchain_core.output_parsers import PydanticToolsParser
 
 # from langchain_core.pydantic_v1 import BaseModel, Field
 from biochatter.llm_connect import Conversation
 
-from biochatter.api_agent.abc import BaseAPIModel, BaseQueryBuilder, BaseTools
+from .abc import BaseAPIModel, BaseQueryBuilder
 
 if TYPE_CHECKING:
     from biochatter.llm_connect import Conversation
 
 
-from typing import Optional
-# Careful as this is not the same as the langchain_core.pydantic_v1
-from pydantic import BaseModel, Field, create_model
+from pydantic import BaseModel, Field
 
 ANNDATA_IO_QUERY_PROMPT = """
 You are a world class algorithm, computational biologist with world leading knowledge
@@ -34,131 +32,194 @@
 NEVER return a code snippet or code itself, instead you have to return a structured output format.
 You will have to create a structured output formats containing method:argument fields.
 
-Here are the possible questions you might be asked:
-<question: output> TBD
-<question: output> TBD
-<question: output> TBD
-
-BASED ON THE DOCUMENTATION below:
-### 1. Reading AnnData Native Formats
-- **HDF5 (.h5ad):**
-  `io.read_h5ad(filename[, backed, as_sparse, ...])`
-  - Reads `.h5ad`-formatted HDF5 file.
-
-- **Zarr:**
-  `io.read_zarr(store)`
-  - Reads from a hierarchical Zarr array store.
-
-### 2. Reading Specific Portions of AnnData
-- **Individual Elements (e.g., obs, varm, etc.):**
-  `io.read_elem(elem)`
-  - Reads an individual element from a store.
-
-- **Backed Mode-Compatible Sparse Dataset:**
-  `io.sparse_dataset(group)`
-  - Generates a sparse dataset class compatible with backed mode.
-
-### 3. Reading Non-Native Formats
-#### 3.1 General Tips
-- Non-native formats may not represent all aspects of AnnData objects.
-- Assembling the AnnData object manually from individual parts may be more successful.
-
-#### 3.2 Supported Formats
-- **CSV:**
-  `io.read_csv(filename[, delimiter, ...])`
-  - Reads `.csv` file.
-
-- **Excel (.xlsx):**
-  `io.read_excel(filename, sheet[, dtype])`
-  - Reads `.xlsx` (Excel) file.
-
-- **HDF5 (.h5):**
-  `io.read_hdf(filename, key)`
-  - Reads `.h5` (HDF5) file.
-
-- **Loom:**
-  `io.read_loom(filename, *[, sparse, cleanup, ...])`
-  - Reads `.loom`-formatted HDF5 file.
-
-- **Matrix Market (.mtx):**
-  `io.read_mtx(filename[, dtype])`
-  - Reads `.mtx` file.
-
-- **Text (.txt, .tab, .data):**
-  `io.read_text(filename[, delimiter, ...])`
-  - Reads `.txt`, `.tab`, or `.data` text files.
-
-- **UMI Tools Matrix:**
-  `io.read_umi_tools(filename[, dtype])`
-  - Reads a gzipped condensed count matrix from UMI Tools.
+You will be asked to read in an anndata object from any anndata api supported format OR
+to concatenate the anndata objects.
+FOR THE MapAnnData, BE SURE TO ALWAYS USE THE variable of the anndata GIVEN IN THE INPUT, REPLACE IT IN THE method_name
+Use the tools available:
 """
-class Tools(BaseTools):
-    tools_params = {}
-    tools_params["io.read_h5ad"] = {
-        "filename": (str, Field(default="dummy.h5ad", description="Path to the .h5ad file")),
-        "backed": (Optional[str], Field(default=None, description="Mode to access file: None, 'r' for read-only")),
-        "as_sparse": (Optional[str], Field(default=None, description="Convert to sparse format: 'csr', 'csc', or None")),
-        "as_sparse_fmt": (Optional[str], Field(default=None, description="Sparse format if converting, e.g., 'csr'")),
-        "index_unique": (Optional[str], Field(default=None, description="Make index unique by appending suffix if needed"))
-        }
-
-    # Parameters for io.read_zarr
-    tools_params["io.read_zarr"] = {
-        "method_name": (str, Field(default="io.read_zarr", description="NEVER CHANGE")),
-        "filename": (str, Field(default="placeholder.zarr", description="Path or URL to the Zarr store"))
-    }
-
-    # Parameters for io.read_csv
-    tools_params["io.read_csv"] = {
-        "method_name": (str, Field(default="io.read_csv", description="NEVER CHANGE")),
-        "filename": (str, Field(default="placeholder.csv", description="Path to the .csv file")),
-        "delimiter": (Optional[str], Field(default=None, description="Delimiter used in the .csv file")),
-        "first_column_names": (Optional[bool], Field(default=None, description="Whether the first column contains names"))
-    }
-
-    # Parameters for io.read_excel
-    tools_params["io.read_excel"] = {
-        "method_name": (str, Field(default="io.read_excel", description="NEVER CHANGE")),
-        "filename": (str, Field(default="placeholder.xlsx", description="Path to the .xlsx file")),
-        "sheet": (Optional[str], Field(default=None, description="Sheet name or index to read from")),
-        "dtype": (Optional[str], Field(default=None, description="Data type for the resulting dataframe"))
-    }
-
-    # Parameters for io.read_hdf
-    tools_params["io.read_hdf"] = {
-        "method_name": (str, Field(default="io.read_hdf", description="NEVER CHANGE")),
-        "filename": (str, Field(default="placeholder.h5", description="Path to the .h5 file")),
-        "key": (Optional[str], Field(default=None, description="Group key within the .h5 file"))
-    }
-
-    # Parameters for io.read_loom
-    tools_params["io.read_loom"] = {
-        "method_name": (str, Field(default="io.read_loom", description="NEVER CHANGE")),
-        "filename": (str, Field(default="placeholder.loom", description="Path to the .loom file")),
-        "sparse": (Optional[bool], Field(default=None, description="Whether to read data as sparse")),
-        "cleanup": (Optional[bool], Field(default=None, description="Clean up invalid entries")),
-        "X_name": (Optional[str], Field(default=None, description="Name to use for X matrix")),
-        "obs_names": (Optional[str], Field(default=None, description="Column to use for observation names")),
-        "var_names": (Optional[str], Field(default=None, description="Column to use for variable names"))
-    }
-
-    # Parameters for io.read_mtx
-    tools_params["io.read_mtx"] = {
-        "method_name": (str, Field(default="io.read_mtx", description="NEVER CHANGE")),
-        "filename": (str, Field(default="placeholder.mtx", description="Path to the .mtx file")),
-        "dtype": (Optional[str], Field(default=None, description="Data type for the matrix"))
-    }
-
-    # Parameters for io.read_text
-    tools_params["io.read_text"] = {
-        "method_name": (str, Field(default="io.read_text", description="NEVER CHANGE")),
-        "filename": (str, Field(default="placeholder.txt", description="Path to the text file")),
-        "delimiter": (Optional[str], Field(default=None, description="Delimiter used in the file")),
-        "first_column_names": (Optional[bool], Field(default=None, description="Whether the first column contains names"))
-    }
-    def __init__(self, tools_params: dict = tools_params):
-        super().__init__()
-        self.tools_params = tools_params
+
+
+class ConcatenateAnnData(BaseAPIModel):
+    """Concatenate AnnData objects along an axis."""
+
+    method_name: str = Field(default="anndata.concat", description="NEVER CHANGE")
+    adatas: list | dict = Field(
+        ...,
+        description="The objects to be concatenated. Either a list of AnnData objects or a mapping of keys to AnnData objects.",
+    )
+    axis: str = Field(
+        default="obs",
+        description="Axis to concatenate along. Can be 'obs' (0) or 'var' (1). Default is 'obs'.",
+    )
+    join: str = Field(
+        default="inner",
+        description="How to align values when concatenating. Options: 'inner' or 'outer'. Default is 'inner'.",
+    )
+    merge: str | Callable | None = Field(
+        default=None,
+        description="How to merge elements not aligned to the concatenated axis. Strategies include 'same', 'unique', 'first', 'only', or a callable function.",
+    )
+    uns_merge: str | Callable | None = Field(
+        default=None,
+        description="How to merge the .uns elements. Uses the same strategies as 'merge'.",
+    )
+    label: str | None = Field(
+        default=None,
+        description="Column in axis annotation (.obs or .var) to place batch information. Default is None.",
+    )
+    keys: list | None = Field(
+        default=None,
+        description="Names for each object being concatenated. Used for column values or appended to the index if 'index_unique' is not None. Default is None.",
+    )
+    index_unique: str | None = Field(
+        default=None,
+        description="Delimiter for making the index unique. When None, original indices are kept.",
+    )
+    fill_value: Any | None = Field(
+        default=None,
+        description="Value used to fill missing indices when join='outer'. Default behavior depends on array type.",
+    )
+    pairwise: bool = Field(
+        default=False,
+        description="Include pairwise elements along the concatenated dimension. Default is False.",
+    )
+
+
+class MapAnnData(BaseAPIModel):
+    """Apply mapping functions to elements of AnnData."""
+
+    method_name: str = Field(
+        default="anndata.obs|var['annotation_name'].map",
+        description=(
+            "ALWAYS ALWAYS ALWAYS REPLACE THE anndata BY THE ONE GIVEN BY THE INPUT"
+            "Specifies the AnnData attribute and operation being performed. "
+            "For example, 'obs.map' applies a mapping function or dictionary to the specified column in `adata.obs`. "
+            "This must always include the AnnData component and the `.map` operation. "
+            "Adapt the component (e.g., 'obs', 'var', etc.) to the specific use case."
+        ),
+    )
+    dics: dict | None = Field(default=None, description="Dictionary to map over.")
+
+
+class ReadH5AD(BaseAPIModel):
+    """Read .h5ad-formatted hdf5 file."""
+
+    method_name: str = Field(default="io.read_h5ad", description="NEVER CHANGE")
+    filename: str = Field(default="dummy.h5ad", description="Path to the .h5ad file")
+    backed: str | None = Field(
+        default=None,
+        description="Mode to access file: None, 'r' for read-only",
+    )
+    as_sparse: str | None = Field(
+        default=None,
+        description="Convert to sparse format: 'csr', 'csc', or None",
+    )
+    as_sparse_fmt: str | None = Field(
+        default=None,
+        description="Sparse format if converting, e.g., 'csr'",
+    )
+    index_unique: str | None = Field(
+        default=None,
+        description="Make index unique by appending suffix if needed",
+    )
+
+
+class ReadZarr(BaseAPIModel):
+    """Read from a hierarchical Zarr array store."""
+
+    method_name: str = Field(default="io.read_zarr", description="NEVER CHANGE")
+    filename: str = Field(
+        default="placeholder.zarr",
+        description="Path or URL to the Zarr store",
+    )
+
+
+class ReadCSV(BaseAPIModel):
+    """Read .csv file."""
+
+    method_name: str = Field(default="io.read_csv", description="NEVER CHANGE")
+    filename: str = Field(
+        default="placeholder.csv",
+        description="Path to the .csv file",
+    )
+    delimiter: str | None = Field(
+        None,
+        description="Delimiter used in the .csv file",
+    )
+    first_column_names: bool | None = Field(
+        None,
+        description="Whether the first column contains names",
+    )
+
+
+class ReadExcel(BaseAPIModel):
+    """Read .xlsx (Excel) file."""
+
+    method_name: str = Field(default="io.read_excel", description="NEVER CHANGE")
+    filename: str = Field(
+        default="placeholder.xlsx",
+        description="Path to the .xlsx file",
+    )
+    sheet: str | None = Field(None, description="Sheet name or index to read from")
+    dtype: str | None = Field(
+        None,
+        description="Data type for the resulting dataframe",
+    )
+
+
+class ReadHDF(BaseAPIModel):
+    """Read .h5 (hdf5) file."""
+
+    method_name: str = Field(default="io.read_hdf", description="NEVER CHANGE")
+    filename: str = Field(default="placeholder.h5", description="Path to the .h5 file")
+    key: str | None = Field(None, description="Group key within the .h5 file")
+
+
+class ReadLoom(BaseAPIModel):
+    """Read .loom-formatted hdf5 file."""
+
+    method_name: str = Field(default="io.read_loom", description="NEVER CHANGE")
+    filename: str = Field(
+        default="placeholder.loom",
+        description="Path to the .loom file",
+    )
+    sparse: bool | None = Field(None, description="Whether to read data as sparse")
+    cleanup: bool | None = Field(None, description="Clean up invalid entries")
+    X_name: str | None = Field(None, description="Name to use for X matrix")
+    obs_names: str | None = Field(
+        None,
+        description="Column to use for observation names",
+    )
+    var_names: str | None = Field(
+        None,
+        description="Column to use for variable names",
+    )
+
+
+class ReadMTX(BaseAPIModel):
+    """Read .mtx file."""
+
+    method_name: str = Field(default="io.read_mtx", description="NEVER CHANGE")
+    filename: str = Field(
+        default="placeholder.mtx",
+        description="Path to the .mtx file",
+    )
+    dtype: str | None = Field(None, description="Data type for the matrix")
+
+
+class ReadText(BaseAPIModel):
+    """Read .txt, .tab, .data (text) file."""
+
+    method_name: str = Field(default="io.read_text", description="NEVER CHANGE")
+    filename: str = Field(
+        default="placeholder.txt",
+        description="Path to the text file",
+    )
+    delimiter: str | None = Field(None, description="Delimiter used in the file")
+    first_column_names: bool | None = Field(
+        None,
+        description="Whether the first column contains names",
+    )
 
 
 class AnnDataIOQueryBuilder(BaseQueryBuilder):
@@ -186,7 +247,7 @@ def create_runnable(
             A Callable object that can execute the query.
 
         """
-        runnable = conversation.chat.bind_tools(query_parameters)
+        runnable = conversation.chat.bind_tools(query_parameters, tool_choice="required")
         return runnable | PydanticToolsParser(tools=query_parameters)
 
     def parameterise_query(
@@ -213,12 +274,27 @@ def parameterise_query(
             AnnDataIOQuery: the parameterised query object (Pydantic model)
 
         """
-        tool_maker = Tools()
-        tools = tool_maker.make_pydantic_tools()
+        tools = [
+            ReadCSV,
+            ReadExcel,
+            ReadH5AD,
+            ReadHDF,
+            ReadLoom,
+            ReadMTX,
+            ReadText,
+            ReadZarr,
+            ConcatenateAnnData,
+            MapAnnData,
+        ]
         runnable = self.create_runnable(
-            conversation=conversation, query_parameters=tools
+            conversation=conversation,
+            query_parameters=tools,
         )
+        query = [
+            ("system", ANNDATA_IO_QUERY_PROMPT),
+            ("human", f"{question}"),
+        ]
         anndata_io_call_obj = runnable.invoke(
-            question,
+            query,
         )
         return anndata_io_call_obj
diff --git a/biochatter/api_agent/formatters.py b/biochatter/api_agent/formatters.py
index efd71ac2..69f5ecef 100644
--- a/biochatter/api_agent/formatters.py
+++ b/biochatter/api_agent/formatters.py
@@ -3,6 +3,7 @@
 from urllib.parse import urlencode
 
 from .abc import BaseAPIModel, BaseModel
+from .anndata_agent import MapAnnData
 
 
 def format_as_rest_call(model: BaseModel) -> str:
@@ -37,7 +38,9 @@ def format_as_python_call(model: BaseAPIModel) -> str:
     params = model.dict(exclude_none=True)
     method_name = params.pop("method_name", None)
     params.pop("question_uuid", None)
-
-    param_str = ", ".join(f"{k}={repr(v)}" for k, v in params.items())
+    if isinstance(model, MapAnnData):
+        param_str = params.pop("dics", {})
+    else:
+        param_str = ", ".join(f"{k}={v!r}" for k, v in params.items())
 
     return f"{method_name}({param_str})"
diff --git a/biochatter/api_agent/scanpy_tl.py b/biochatter/api_agent/scanpy_tl.py
index 37287720..21645fd3 100644
--- a/biochatter/api_agent/scanpy_tl.py
+++ b/biochatter/api_agent/scanpy_tl.py
@@ -2,11 +2,12 @@
 
 from collections.abc import Callable
 from types import ModuleType
-from typing import TYPE_CHECKING
 
 from langchain_core.output_parsers import PydanticToolsParser
 
-from .abc import BaseQueryBuilder, BaseAPIModel
+from biochatter.llm_connect import Conversation
+
+from .abc import BaseAPIModel, BaseQueryBuilder
 from .generate_pydantic_classes_from_module import generate_pydantic_classes
 from biochatter.llm_connect import Conversation
 
@@ -61,6 +62,7 @@
    tl.sim
 """
 
+
 class ScanpyTlQueryBuilder(BaseQueryBuilder):
     """A class for building an ScanpyTlQuery object."""
 
@@ -69,8 +71,7 @@ def create_runnable(
         query_parameters: list["BaseAPIModel"],
         conversation: Conversation,
     ) -> Callable:
-
-        runnable = conversation.chat.bind_tools(query_parameters, tool_choice = "required")
+        runnable = conversation.chat.bind_tools(query_parameters, tool_choice="required")
         return runnable | PydanticToolsParser(tools=query_parameters)
 
     def parameterise_query(
@@ -104,7 +105,8 @@ def parameterise_query(
             tools = generate_pydantic_classes(module)
 
         runnable = self.create_runnable(
-            conversation=conversation, query_parameters=tools,
+            conversation=conversation,
+            query_parameters=tools,
         )
 
         query = [
diff --git a/docs/images/api-calling-benchmark-comparison.png b/docs/images/api-calling-benchmark-comparison.png
new file mode 100644
index 00000000..da008b9d
Binary files /dev/null and b/docs/images/api-calling-benchmark-comparison.png differ
diff --git a/docs/images/api-calling-model-comparison.png b/docs/images/api-calling-model-comparison.png
new file mode 100644
index 00000000..a9e14a57
Binary files /dev/null and b/docs/images/api-calling-model-comparison.png differ
diff --git a/docs/images/api-calling-task-type-comparison.png b/docs/images/api-calling-task-type-comparison.png
new file mode 100644
index 00000000..97054305
Binary files /dev/null and b/docs/images/api-calling-task-type-comparison.png differ
diff --git a/docs/scripts/api_analysis.py b/docs/scripts/api_analysis.py
new file mode 100644
index 00000000..0531672a
--- /dev/null
+++ b/docs/scripts/api_analysis.py
@@ -0,0 +1,181 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+def analyze_api_calling():
+    """Analyze and plot results from the API calling benchmarks."""
+    
+    # Read and process Python API calling results
+    python_results = preprocess_results("benchmark/results/python_api_calling.csv", "Python API")
+    python_reduced_results = preprocess_results(
+        "benchmark/results/python_api_calling_reduced.csv", 
+        "Python API (Reduced)"
+    )
+    
+    # Combine Python results
+    all_results = pd.concat([python_results, python_reduced_results])
+    
+    # Plot results
+    plot_model_family_comparison(all_results)
+    plot_benchmark_comparison(all_results)
+    plot_task_type_comparison(all_results)
+    save_summary_statistics(all_results)
+
+def preprocess_results(filepath: str, benchmark_type: str) -> pd.DataFrame:
+    """Preprocess results from a single benchmark file."""
+    results = pd.read_csv(filepath)
+    
+    # Calculate accuracy
+    results["score_possible"] = results["score"].apply(
+        lambda x: float(x.split("/")[1])
+    )
+    results["scores"] = results["score"].apply(lambda x: x.split("/")[0])
+    results["score_achieved"] = results["scores"].apply(
+        lambda x: (np.mean([float(score) for score in x.split(";")]) if ";" in x else float(x))
+    )
+    results["accuracy"] = results["score_achieved"] / results["score_possible"]
+    
+    # Add benchmark type
+    results["benchmark_type"] = benchmark_type
+    
+    # Add task type analysis and merge similar categories
+    results["task_type"] = results["subtask"].apply(lambda x: x.split(" - ")[0].split(":")[-1])
+    
+    # Merge similar categories
+    results["task_type"] = results["task_type"].replace({
+        "abbreviations": "abbreviation",
+        "general_question": "general",
+        "explicit_variable_names": "specific",
+    })
+    
+    return results
+
+def plot_model_family_comparison(results: pd.DataFrame) -> None:
+    """Create violin plot comparing individual models across all benchmarks."""
+    sns.set_theme(style="whitegrid")
+    plt.figure(figsize=(12, 6))
+    
+    # # Define model order (from oldest to newest)
+    # model_order = [
+    #     "gpt-3.5-turbo-0125",
+    #     "gpt-4-0613", 
+    #     "gpt-4-0125-preview",
+    #     "gpt-4-1106-preview",
+    #     "gpt-4-turbo-2024-04-09",
+    #     "gpt-4o-2024-08-06",
+    #     "gpt-4o-2023-11-20",
+    #     "gpt-4o-mini-2024-07-18"
+    # ]
+    # # Ensure model order has no duplicates and only includes models present in the data
+    # model_order = [m for m in model_order if m in results['model_name'].unique()]
+    
+    sns.violinplot(
+        x="model_name",
+        y="accuracy",
+        hue="benchmark_type",
+        data=results,
+        cut=0,  # Don't extend beyond observed data
+        # order=model_order
+    )
+    
+    plt.title("API Calling Performance by Model and Benchmark Type")
+    plt.xlabel("Model")
+    plt.ylabel("Accuracy")
+    plt.xticks(rotation=45, ha='right')
+    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
+    
+    plt.savefig(
+        "docs/images/api-calling-model-comparison.png",
+        bbox_inches="tight",
+        dpi=300
+    )
+    plt.close()
+
+def plot_benchmark_comparison(results: pd.DataFrame) -> None:
+    """Create violin plot comparing benchmark types."""
+    sns.set_theme(style="whitegrid")
+    plt.figure(figsize=(8, 6))
+    
+    sns.violinplot(
+        x="benchmark_type",
+        y="accuracy",
+        data=results,
+        cut=0  # Don't extend beyond observed data
+    )
+    
+    plt.title("Performance Comparison Across API Calling Benchmarks")
+    plt.xlabel("Benchmark Type")
+    plt.ylabel("Accuracy")
+    plt.xticks(rotation=45)
+    
+    plt.savefig(
+        "docs/images/api-calling-benchmark-comparison.png",
+        bbox_inches="tight",
+        dpi=300
+    )
+    plt.close()
+
+def plot_task_type_comparison(results: pd.DataFrame) -> None:
+    """Create violin plot comparing performance across different task types."""
+    sns.set_theme(style="whitegrid")
+    plt.figure(figsize=(12, 6))
+    
+    sns.violinplot(
+        x="task_type",
+        y="accuracy",
+        hue="benchmark_type",
+        data=results,
+        cut=0  # Don't extend beyond observed data
+    )
+    
+    plt.title("API Calling Performance by Task Type")
+    plt.xlabel("Task Type")
+    plt.ylabel("Accuracy")
+    plt.xticks(rotation=45, ha='right')
+    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
+    
+    plt.savefig(
+        "docs/images/api-calling-task-type-comparison.png",
+        bbox_inches="tight",
+        dpi=300
+    )
+    plt.close()
+
+def save_summary_statistics(results: pd.DataFrame) -> None:
+    """Calculate and save summary statistics."""
+    # Summary by model and benchmark type
+    summary = results.groupby(["benchmark_type", "model_name"])["accuracy"].agg([
+        "mean",
+        "std",
+        "min", 
+        "max",
+        "count"
+    ]).round(3)
+    
+    summary.to_csv("benchmark/results/processed/api_calling_summary.csv")
+    
+    # Overall benchmark comparison
+    benchmark_summary = results.groupby("benchmark_type")["accuracy"].agg([
+        "mean",
+        "std",
+        "min",
+        "max",
+        "count"
+    ]).round(3)
+    
+    benchmark_summary.to_csv("benchmark/results/processed/api_calling_benchmark_summary.csv")
+    
+    # Add task type summary
+    task_summary = results.groupby(["benchmark_type", "task_type"])["accuracy"].agg([
+        "mean",
+        "std",
+        "min",
+        "max",
+        "count"
+    ]).round(3)
+    
+    task_summary.to_csv("benchmark/results/processed/api_calling_task_summary.csv")
+
+if __name__ == "__main__":
+    analyze_api_calling()
\ No newline at end of file


Test patch:
diff --git a/benchmark/conftest.py b/benchmark/conftest.py
index 0514de0a..7bde2c73 100644
--- a/benchmark/conftest.py
+++ b/benchmark/conftest.py
@@ -17,20 +17,24 @@
 from .load_dataset import get_benchmark_dataset
 
 # how often should each benchmark be run?
-N_ITERATIONS = 1
+N_ITERATIONS = 3
 
 # which dataset should be used for benchmarking?
 BENCHMARK_DATASET = get_benchmark_dataset()
 
 # which models should be benchmarked?
 OPENAI_MODEL_NAMES = [
-    # "gpt-3.5-turbo-0125",
-    # "gpt-4-0613",
-    # "gpt-4-0125-preview",
-    # "gpt-4-turbo-2024-04-09",
+    "gpt-3.5-turbo-0125",
+    "gpt-4-0613",
+    "gpt-4-1106-preview",
+    "gpt-4-0125-preview",
+    "gpt-4-turbo-2024-04-09",
     # "gpt-4o-2024-05-13",
     "gpt-4o-2024-08-06",
-    # "gpt-4o-mini-2024-07-18",
+    "gpt-4o-2024-11-20",
+    "gpt-4o-mini-2024-07-18",
+    # "o1-preview-2024-09-12",
+    # "o1-mini-2024-09-12",
 ]
 
 ANTHROPIC_MODEL_NAMES = [
diff --git a/test/test_api_agent.py b/test/test_api_agent.py
index ee860f80..e0cebfd4 100644
--- a/test/test_api_agent.py
+++ b/test/test_api_agent.py
@@ -4,6 +4,7 @@
 from unittest.mock import MagicMock, patch
 
 import pytest
+from langchain_core.output_parsers import PydanticToolsParser
 from pydantic import BaseModel
 
 from biochatter.api_agent.abc import (
@@ -12,7 +13,8 @@
     BaseQueryBuilder,
 )
 
-from biochatter.api_agent.anndata_agent import AnnDataIOQueryBuilder
+
+from biochatter.api_agent.anndata_agent import AnnDataIOQueryBuilder, ANNDATA_IO_QUERY_PROMPT
 from biochatter.api_agent.scanpy_pp_reduced import ScanpyPpQueryBuilder
 
 from biochatter.api_agent.api_agent import APIAgent
@@ -35,12 +37,9 @@
 from biochatter.api_agent.scanpy_pl import (
     ScanpyPlQueryBuilder,
 )
-
-from biochatter.api_agent.scanpy_tl import ScanpyTlQueryBuilder
+from biochatter.api_agent.scanpy_tl import SCANPY_QUERY_PROMPT, ScanpyTlQueryBuilder
 from biochatter.llm_connect import Conversation, GptConversation
 
-from langchain_core.output_parsers import PydanticToolsParser
-
 
 def conversation_factory():
     conversation = GptConversation(
@@ -492,15 +491,51 @@ def mock_create_runnable(self):
             mock.return_value = mock_runnable
             yield mock_runnable
 
-    def test_create_runnable(self):
-        pass
+    @patch("biochatter.llm_connect.GptConversation")
+    def test_create_runnable(self, mock_conversation):
+        # Mock the list of Pydantic classes as a list of Mock objects
+        class MockTool1(BaseModel):
+            param1: str
+
+        class MockTool2(BaseModel):
+            param2: int
+
+        mock_generated_classes = [MockTool1, MockTool2]
+
+        # Mock the conversation object and LLM
+        mock_conversation_instance = mock_conversation.return_value
+        mock_llm = MagicMock()
+        mock_conversation_instance.chat = mock_llm
+
+        # Mock the LLM with tools
+        mock_llm_with_tools = MagicMock()
+        mock_llm.bind_tools.return_value = mock_llm_with_tools
+
+        # Mock the chain
+        mock_chain = MagicMock()
+        mock_llm_with_tools.__or__.return_value = mock_chain
+
+        # Act
+        builder = AnnDataIOQueryBuilder()
+        result = builder.create_runnable(
+            query_parameters=mock_generated_classes,
+            conversation=mock_conversation_instance,
+        )
+
+        # Assert
+        mock_llm.bind_tools.assert_called_once_with(mock_generated_classes, tool_choice="required")
+        mock_llm_with_tools.__or__.assert_called_once_with(
+            PydanticToolsParser(tools=mock_generated_classes),
+        )
+        # Verify the returned chain
+        assert result == mock_chain
 
     def test_parameterise_query(self, mock_create_runnable):
         # Arrange
         query_builder = AnnDataIOQueryBuilder()
         mock_conversation = MagicMock()
         question = "read a .h5ad file into an anndata object."
-        expected_input = f"{question}"
+        expected_input = [("system", ANNDATA_IO_QUERY_PROMPT), ("human", question)]
         mock_query_obj = MagicMock()
         mock_create_runnable.invoke.return_value = mock_query_obj
 
@@ -511,6 +546,7 @@ def test_parameterise_query(self, mock_create_runnable):
         mock_create_runnable.invoke.assert_called_once_with(expected_input)
         assert result == mock_query_obj
 
+
 class TestScanpyPpQueryBuilder:
     @pytest.fixture
     def mock_create_runnable(self):
@@ -540,11 +576,12 @@ def test_parameterise_query(self, mock_create_runnable):
         mock_create_runnable.invoke.assert_called_once_with(expected_input)
         assert result == mock_query_obj
 
-class TestScanpyTLQueryBuilder:
+
+class TestScanpyTlQueryBuilder:
     @pytest.fixture
     def mock_create_runnable(self):
         with patch(
-            "biochatter.api_agent.anndata.AnnDataIOQueryBuilder.create_runnable",
+            "biochatter.api_agent.scanpy_tl.ScanpyTlQueryBuilder.create_runnable",
         ) as mock:
             mock_runnable = MagicMock()
             mock.return_value = mock_runnable
@@ -582,24 +619,25 @@ class MockTool2(BaseModel):
         )
 
         # Assert
-        mock_llm.bind_tools.assert_called_once_with(mock_generated_classes, tool_choice = "required")
+        mock_llm.bind_tools.assert_called_once_with(mock_generated_classes, tool_choice="required")
         mock_llm_with_tools.__or__.assert_called_once_with(
-            PydanticToolsParser(tools=mock_generated_classes)
+            PydanticToolsParser(tools=mock_generated_classes),
         )
         # Verify the returned chain
         assert result == mock_chain
 
     def test_parameterise_query(self, mock_create_runnable):
         # Arrange
-        query_builder = AnnDataIOQueryBuilder()
+        query_builder = ScanpyTlQueryBuilder()
         mock_conversation = MagicMock()
         question = "i want to run PCA on my data"
-        expected_input = f"{question}"
+        expected_input = [("system", SCANPY_QUERY_PROMPT), ("human", question)]
         mock_query_obj = MagicMock()
         mock_create_runnable.invoke.return_value = mock_query_obj
+        module = MagicMock()
 
         # Act
-        result = query_builder.parameterise_query(question, mock_conversation)
+        result = query_builder.parameterise_query(question, mock_conversation, module)
 
         # Assert
         mock_create_runnable.invoke.assert_called_once_with(expected_input)